Efficient particle filtering using patch matching for real-time self localization

In this paper we present a novel approach for 3D camera self localization, that uses template matching in a particle filter framework. We propose a tracking scheme that exploits the matching scores between image patches to design the likelihood function of the filter observation model. Indeed, by representing a template image with an ensemble of patches, the method is robust with respect to variations such as local appearance variation, partial occlusion, and scale changes. Experiment results on tracking a hand-held camera have shown that the proposed approach provides more accurate tracking, especially for fast motion or long-term partial occlusions. Comparisons have been made with existing methods; results have shown that the proposed scheme has provided an improved tracking accuracy at the cost of more computations.